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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional Exam
Exam Number:	Databricks-Machine-Learning-Professional
Exam Price:	$200 USD
Related Certifications:	Databricks Certified Machine Learning Associate
Exam Format:	Multiple choice, Multi-select
Real Exam Qty:	59
Certificate Validity Period:	2 years
Exam Duration:	120 minutes
Passing Score:	Not publicly disclosed (approximately 70%)
Available Languages:	English
Recommended Training:	Machine Learning at Scale Advanced Machine Learning Operations
Exam Registration:	Databricks Certification Portal
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online proctored or in-person test center
Pre Condition:	No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python
Official Syllabus URL:	https://www.databricks.com/learn/certification/machine-learning-professional

>> Databricks-Machine-Learning-Professional시험유형 <<

Databricks-Machine-Learning-Professional시험유형 덤프에는 ExamName} 시험문제의 모든 유형이 포함

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Databricks Databricks-Machine-Learning-Professional 시험요강:

주제	소개
주제 1	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
주제 2	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
주제 3	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
주제 4	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
주제 5	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
주제 6	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
주제 7	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
주제 8	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry

최신 ML Data Scientist Databricks-Machine-Learning-Professional 무료샘플문제 (Q83-Q88):

질문 # 83
A machine learning engineer is migrating a machine learning pipeline to use Databricks Machine Learning. The pipeline needs to automatically refresh its model each time it runs. The project is attached to the existing model model_name in the MLflow Model Registry.
They are using the following code block as part of their solution:

Which statement describes the impact of the registered_model_name=model_name parameter and argument given that model_name already exists in the MLflow Model Registry?

A. It identifies the name of the logged model in the MLflow Experiment.
B. It registers the new version of the model_name model in the MLflow Model Registry.
C. It avoids the need to specify the model name in the subsequent required call to mflow.register_model.
D. It registers a new model called model_name in the MLflow Model Registry.

정답：B

설명：
When using registered_model_name=model_name in mlflow.spark.log_model(), MLflow automatically registers the logged model under the specified model name. If that model name already exists in the MLflow Model Registry, MLflow creates a new version of that existing registered model rather than a new model entry. This enables automatic versioning and continuous model refresh with each pipeline run.

질문 # 84
A Machine Learning Engineer has automated a model retraining job in Databricks. Each scheduled run trains multiple candidate models with new sales data and logs all runs with MLflow.
The goal is to select and register the best-performing model at the end of each cycle to ensure optimal forecast accuracy. Which approach will meet this goal?

A. Register the first model that has an evaluation metric better than the previously deployed model.
B. Register the model with the lowest training loss regardless of validation metrics.
C. Register the best model based on the primary evaluation metric.
D. Register all models from the run since all of them are trained on newer data and hence will be more accurate.

정답：C

설명：
Selecting and registering the model that performs best on the primary evaluation metric ensures that only the highest-quality model is promoted at each retraining cycle. This approach aligns with MLOps best practices by basing promotion decisions on objective performance criteria rather than training order or assumptions about data freshness.

질문 # 85
What is the main purpose of the Databricks Feature Store?

A. Storing trained models
B. Centralizing reusable ML features
C. Replacing Spark ML pipelines
D. Storing raw data

정답：B

설명：
Feature Store allows teams to:
share features
avoid training/serving skew
maintain feature lineage.

질문 # 86
Why are Delta tables often used to store machine learning features?

A. They support schema enforcement and time travel
B. They allow faster GPU training
C. They reduce model size
D. They replace Spark DataFrames

정답：A

설명：
Delta Lake provides:
ACID transactions
time travel
schema enforcement
These are essential for reproducible ML pipelines.

질문 # 87
A Machine Learning Engineer has a computer vision model in Databricks Model Serving that obscures sensitive data from images. Internal teams use the model throughout the work week when they request access to new files. Recently model users complained that the model takes much longer in the morning. This coincides with when people arrive at work and request files for the day. When the engineer reviews the endpoint health metrics, they see P50 model latency peaks around 9AM at 20 seconds. Request rate also peaks at 9AM at 15 requests/second. The GPU utilization is over 60%, GPU memory usage over 50%, and provisioned concurrency at 4 throughout the day. What can the engineer do to reduce user wait time when request rate peaks at 9AM each morning?

A. The endpoint is constrained by its ability to handle simultaneous requests. Define a rate_1imit on the endpoint to spread out the requests over the course of the day.
B. The endpoint is constrained by its ability to handle simultaneous requests. Scale the endpoint horizontally by editing the endpoint workload_size from "Small" to "Medium" to increase the model concurrency.
C. The endpoint is constrained by the GPU. Scale the endpoint vertically by changing the endpoint workload_type from "GPU_SMALL" to "GPU_MEDIUM".
D. The endpoint is constrained by its ability to handle simultaneous requests. Enable "scale_to_zero" on the endpoint to spin up additional endpoints quickly to handle the requests.

정답：B

설명：
The symptoms indicate a concurrency bottleneck during the 9AM traffic spike: request rate increases sharply, latency jumps, and the endpoint is already running at a fixed provisioned concurrency of 4. Increasing workload_size scales the endpoint horizontally to handle more concurrent requests, reducing queueing and bringing down user-perceived wait time during peak demand.

질문 # 88
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